knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(tidyverse)
library(here)
library(sf)
library(tmap)

cmd-shift-enter shortcut for running the current code chunk cmd-enter shortcut for running the current line

read in data

cmd-option-i shortcut for creating a code chunk

sf_trees <- read_csv(here('data', 'sf_trees', 'sf_trees.csv'))

Part 1: wrangling and ggplot review

# method 1: group_by() %>% summarize()
sf_trees %>% 
  group_by(legal_status) %>% 
  summarize(tree_count = n())
## # A tibble: 10 × 2
##    legal_status                 tree_count
##    <chr>                             <int>
##  1 DPW Maintained                   141725
##  2 Landmark tree                        42
##  3 Permitted Site                    39732
##  4 Planning Code 138.1 required        971
##  5 Private                             163
##  6 Property Tree                       316
##  7 Section 143                         230
##  8 Significant Tree                   1648
##  9 Undocumented                       8106
## 10 <NA>                                 54
# method 2
top_5_status <- sf_trees %>% 
  count(legal_status) %>% 
  drop_na(legal_status) %>% 
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% 
  slice_max(tree_count, n = 5) %>% 
  arrange(-tree_count)

Make a graph of the top 5 from above:

ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
  geom_col(fill = 'darkgreen') +
  labs(x = 'legal status', y = 'Tree count') +
  coord_flip() +
  theme_minimal()

Example 2:Only going to keep observations where legal status is ‘permitted site’ and caretaker is ’MTA, and store as permitted_data_df

shift-cmd-c to comment/uncomment quickly

# sf_trees$legal_status %>% unique()
# unique(sf_trees$legal_status)

permitted_data_df <- sf_trees %>% 
  filter(legal_status == "Permitted Site", caretaker == "MTA")

Example 3: Only keep blackwood acacia trees, and then only keep certain columns

blackwood_acacia_df <- sf_trees %>% 
  filter(str_detect(species, 'Blackwood Acacia')) %>% 
  select(legal_status, date, lat = latitude, lon = longitude)

### make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
  geom_point(color = 'darkgreen')

Example 4: use tidyr::separate()

sf_trees_sep <- sf_trees %>% separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')

Example 5: use tidyr::unite()

ex_5 <- sf_trees %>% 
  unite('id_status', tree_id, legal_status, sep = "_COOL_")

Part 2: make some mpas

Step 1: Convert the lat/lon to spatial point, st_as_sf()

blackwood_acacia_sf <- blackwood_acacia_df %>% 
  drop_na(lat, lon) %>% 
  st_as_sf(coords = c('lon', 'lat'))

### we need to tell R what the coordinate reference is
st_crs(blackwood_acacia_sf) <- 4326 

ggplot(data = blackwood_acacia_sf) +
  geom_sf(color = 'darkgreen') +
  theme_minimal()

Read in the SF shapefile and add to map

sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))

sf_map_transform <- st_transform(sf_map, 4326)

ggplot(data=sf_map_transform) +
  geom_sf()

Combine the maps!

ggplot() +
  geom_sf(data=sf_map,
          size=.1,
          color='darkgrey') +
  geom_sf(data=blackwood_acacia_sf,
          color='darkgreen',
          size = 0.5) +
  theme_void() +
  labs(title = 'Blackwood Acacias in SF')

Now an interactive map

tmap_mode('view')

tm_shape(blackwood_acacia_sf) +
  tm_dots()